Integrity of the arcuate fasciculus in patients with schizophrenia with auditory verbal hallucinations: A DTI-tractography study
Bibliographic record
Abstract
Auditory verbal hallucinations (AVH) of schizophrenia are associated with a disrupted connectivity between frontal and temporoparietal language areas. We hypothesized that this dysconnectivity is underpinned by white matter abnormalities in the left arcuate fasciculus , the main fiber bundle connecting speech production and perception areas. We therefore investigated the relationship between AVH severity and the integrity of the arcuate fasciculus measured by diffusion tensor imaging (DTI) tractography in patients with schizophrenia. Thirty-eight patients with treatment-resistant schizophrenia were included: 26 presented with daily severe treatment-resistant AVH, 12 reported prominent negative symptoms and no AVH. Fractional anisotropy (FA) was measured along the length of the left and right anterior arcuate fasciculi and severity of AVH was assessed using P3 PANSS item. FA values were significantly higher in the left arcuate fasciculus in patients with AVH than in no AVH patients (F (1,35) = 3.86; p = 0.05). No difference was observed in the right arcuate fasciculus. There was a significant positive correlation between FA value in the left arcuate fasciculus and the severity of AVH (r = 0.36; p = 0.02). No correlation was observed between FA values and PANSS total score suggesting a specific relationship between AVH severity and the left arcuate fasciculus integrity. These results support the hypothesis of a relationship between left frontotemporal connectivity and AVH in patients with schizophrenia and suggest that whilst a disruption of frontotemporal connectivity might be present to ensure the emergence of AVH, more severe anatomical alterations may prevent the occurrence of AVH in patients with schizophrenia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".